AI inventory management: a practical route for SMEs

Too much stock ties up cash. Too little stock costs sales and customers. For many small and mid-sized businesses, this remains a daily gut feeling rather than a calculation. Spreadsheets, manual counts and the experience of a warehouse employee decide what gets ordered.

AI inventory management promises to reduce that uncertainty. But most articles on the topic are written by software vendors mainly trying to sell their own platform. They quote impressive percentages without a source, or suggest buying an enterprise system like SAP or Blue Yonder right away. For an SME with a few thousand SKUs, that is rarely realistic.

This article focuses on what actually is realistic: what AI concretely does with your inventory data, how to connect it to Exact Online or AFAS, what it roughly costs at SME scale, and when you are better off waiting.

What AI actually does in inventory management

At its core, AI inventory management recognizes patterns in data that a person cannot easily oversee: sales history, seasonal effects, supplier lead times and current stock levels per location or channel.

In practice, this comes down to a few concrete tasks:

1. Forecasting demand per SKU, based on historical sales, season and trends, instead of a fixed safety stock.
2. Automatically adjusting reorder points as demand patterns change, without anyone manually editing a spreadsheet formula.
3. Flagging anomalies, such as a product that suddenly sells three times faster than normal, or a supplier that structurally delivers late.
4. Distributing stock across multiple locations or sales channels, so you are not sitting on stock in one place while turning down orders in another.

The difference with a traditional ERP report is that AI does this continuously and per SKU, instead of once a month in a manual overview.

Six concrete use cases for SMEs

Generic demand forecasting is what every vendor already talks about. The situations below are more specific and recognizable for real small business contexts.

1. Webshop with seasonal peaks

A webshop selling, say, garden products or outdoor toys sees strong peaks in spring and a quiet winter. AI that combines historical sales data with weather forecasts and the marketing calendar can warn in time when reordering is about to become too late for the season.

2. Technical wholesaler with spare parts

At an installer or technical wholesaler, thousands of parts sit on the shelf that may not sell for months and then are suddenly needed for a repair. AI can distinguish between "slow but critical" and "slow and redundant," something a simple ABC analysis in a spreadsheet cannot do.

3. Food and hospitality with expiry dates

For businesses working with perishables, overstock is money thrown away immediately. AI models that link purchasing to expiry dates and sales speed per day of the week help fine-tune order quantities to what actually sells before it spoils.

4. Multiple sales channels sharing the same stock

Many SMEs sell through their own webshop, a marketplace and a physical store simultaneously, from the same stock pool. Without central, up-to-date synchronization, this leads to overselling. AI-driven synchronization keeps stock per channel current and can automatically pause a channel temporarily when a shortage is imminent.

5. Automatic purchase suggestions to suppliers

Instead of a buyer manually going through every SKU each week, AI generates an order proposal: which item, what quantity, from which supplier, accounting for lead time and minimum order quantity. The human approves, rather than calculating everything themselves.

6. Early warning of supplier problems

AI that tracks lead times per supplier notices sooner than a person when a supplier is structurally delivering later than agreed. That gives room to adjust in time, instead of finding out a delivery is late only once stock has already run out.

Approach: how to implement this step by step

Most SMEs already have the data, just not connected or structured. A realistic approach consists of four phases.

Phase 1: get data in order and connected

Before AI can predict anything meaningful, the basics need to be right: consistent SKU codes, up-to-date stock levels and sales data going back at least 12 to 24 months. For many Dutch SMEs this means connecting to the ERP system, usually Exact Online or AFAS. Our guide to connecting Exact Online to AI describes the technical steps in detail.

Phase 2: start small with a clear scope

Pick one product group or one location as a pilot. Compare the AI forecast alongside your current way of working for a few weeks, without acting on it immediately. That way you see concretely whether the forecast beats your current approach before you switch over.

Phase 3: automated signals, human decides

Initially, let AI make proposals instead of ordering automatically. A buyer who approves a proposal builds trust and can correct cases where the AI misses something, like an upcoming promotion that isn't in the historical data.

Phase 4: scale up and automate further

Once forecasts are demonstrably better than the old approach, expand to more product groups and, where desired, to automatic orders below a set threshold. For this kind of broader process automation, we often look together with clients at AI agents that don't just flag issues but also take action themselves, for example preparing a draft purchase order.

An AI advisor who guides the whole trajectory prevents you from investing in a tool that doesn't fit your ERP system or workflow.

What does AI inventory management cost for an SME

Concrete figures are scarce in existing articles on this topic. The amounts below are indications based on typical SME projects, not quotes.

The biggest cost item is usually not the AI itself, but cleaning up and connecting data from existing systems. A free AI scan gives you a concrete picture in a short time of where you stand now and what a realistic first step would be, before you commit to a project.

When AI inventory management does not (yet) pay off

Not every SME benefits from AI inventory management, at least not yet.

• Few SKUs, stable demand. With fewer than a few dozen active SKUs and predictable demand, a well-maintained spreadsheet is often enough. The value of AI lies in scale and complexity.
• Unreliable base data. If stock levels in the ERP system are structurally wrong, fix that first. AI on top of bad data produces bad forecasts, not less bad ones.
• Highly variable assortment. Businesses that constantly sell new, one-off products without sales history get little value from forecasting models that rely on historical patterns.
• Limited volume, limited impact. If stock value and order volume are low, the investment may not outweigh the savings. Run the numbers before you start.

In these cases, it's often more sensible to first invest in cleaner data and simpler automation, and add AI forecasting later.

Data, privacy and accountability

Inventory data rarely contains personal data, but once you connect customer orders, returns or supplier information to an AI model, it's worth deciding in advance where that data is processed and how long it's retained. If you work with an external AI vendor, explicitly ask for a data processing agreement and where the data is hosted.

A second point is accountability for automatic orders. Once an AI system independently places orders above a certain amount, you want to agree in advance who checks that and who intervenes if something goes wrong, for example a misread demand spike leading to a wildly oversized order. This kind of agreement belongs in the implementation project from the start, not as an afterthought.

Frequently asked questions

Does AI inventory management work with Exact Online or AFAS?

Yes, both systems offer API connections that make sales and stock data available to an AI model. The connection itself requires custom work, depending on how your administration is set up.

How much historical data do I need at minimum?

For reliable seasonal patterns you need at least 12 months of sales data; 24 months gives a more reliable picture, especially with strong seasonal effects.

Does AI replace my buyer or warehouse staff?

Not in most SME projects. AI takes over the calculation and flagging work; the buyer still makes decisions about exceptions, new suppliers and strategic choices.

Can I start small without building a full ERP connection right away?

Yes. You can start with an export of your current stock and sales data to test whether AI forecasting adds value, before investing in a full connection.

What if my inventory data is messy?

Start by cleaning up SKU codes and stock levels. An AI scan maps out where the biggest data quality issues are, so you know where to start first.

Next step

AI inventory management isn't a matter of flipping a switch. It's a combination of clean data, the right connections and a phased approach that fits your stock volume and systems.

Want to know whether it already pays off for your business, and what the first concrete step would be? Take the free AI scan or schedule a no-obligation introduction via our contact form. Curious how AI can speed up other processes in your business too? Also read 5 processes SMEs automate with AI agents or find out what an AI agent roughly costs. For personal advice on the best approach for your situation, reach out to our AI consultancy.